Projectors or illuminators are often used to project infra-red light (about 700 to 2500 nm wavelength for near-infra-red (NIR) onto an object and then use a sensor (or camera) to detect the light reflecting from the object in order to form images of the object. The images then may be used for a number of applications including biometric detection for security authorization purposes such as with face detection or iris scanning recognition. These detection systems may be used to authorize a user to unlock many different objects such as physical doors, computers, computer files, or other electronic devices to name a few examples. Such NIR systems also may be used for eye tracking and other object detection operations such as with motion detection related-games or artificial intelligence (AI), computer vision, and so forth. In these systems, the sensed reflections from the NIR illuminator are used to form an IR or NIR image with specific characteristics needed to perform the desired detection or to use the image for other applications. The cameras that generate images of a user's face to use the image to authorize access to something may be referred to herein as a face login camera.
The conventional NIR illuminator devices use LED illuminators. These conventional illuminators, however, often suffer from a loss of IR signal towards the edges and corners of the image due to fall off (e.g., reduced radiation intensity) of the illuminator, lens shading, image sensor aperture limitations (where the aperture at the camera sensor is not wide enough to capture sufficient light near the edges of the aperture), and angular effects of the IR band pass filter at the sensor (or camera) that permit too much ambient light into the camera. At the same time, the center of the image may be too bright (too much light intensity or radiation) due to too much concentration of light at the center of the image, and so much so that the center of the image may be washed out by the light intensity.
Attempts to compensate for these difficulties are conventionally performed by using digital gain (or in other words, lens shading correction for example) when the IR image is analyzed, displayed, and/or used to provide data to improve an RGB or RGBD (depth) image for example. However, for those applications that typically and automatically analyze signal-to-noise ratio (SNR) on an image, such as with face detection for example, the loss of IR signal also corresponds to a loss of SNR in the corners and edges of the image, causing some systems to fail to meet performance needs of the application. Thus, while the digital gain adjustments may adjust for the extreme high and low light intensity areas on the image providing adequate light intensity values for those areas, the digital gain adjustments cannot compensate for the loss of SNR.